A Website Dedicated to Jobseekers

Hacker News

I have always found job search overwhelming & stressful. I created a website hoping to make some parts of a job hunt easier. The website showcases the tools & resources available to build your profile. Second, it features the websites out there which can aid your job search split up into categories. Finally, a curated list company-specific resource link tool which you can use for your job search, salary negotiations & interview prep. In my opinion, this can be useful to any job seeker out there to see all the possible resources & tools out there. Hope you find it useful & open to any feedback! [https://www.myjobsherpa.com](https://www.myjobsherpa.com)

Visit product View on Hacker NewsDec 7, 2022

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Actual performance

2points

3comments

Did not reach leaderboard

Launch Intel predictions

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Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released

63%63% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

46%46% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Indie HackersIH features products with proven revenue · Strong signals: created · Missing: supports, reddit linkedin, podcasting

45%45% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal

37%37% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: open · Missing: mac, agents, macos

33%33% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

9%9% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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